However, these graph augmentation approaches mentioned above pose some inevitable problems.
For one thing, the time consumption is increased when applying a complex augmentation algorithm.
For another thing, it disturbs the intrinsic structure of the original graph to a certain extent, accompanied with the risk of semantic drift [1]}, and it is difficult to determine whether the augmented graph is positively correlated with the original graph [2]}.
The majority of GCL methods follow the InfoMax [3]} principle, and aim to maximize mutual information of the corresponding graphs (or nodes) under different views of augmentation.
To correct the potential semantic drift introduced by these graph data augmentations, encoders need to learn an extra nonlinear transformation to rebuild the mapping between the augmented graph and the original graph, which leads to the slow convergence of training.
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